Related papers: Predicting new superconductors and their critical …
Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of…
We develop the multi-band BCS model of superconductivity in the ultrathin films using the orthogonal tight-binding approximation for constructing the electron wavefunctions. This allows for relatively simple determination of the band…
High quality Rb0.8Fe1.6+xSe2 single crystals are grown by a one step melting growth method. Superconductivity has been observed in all samples with x more than 0.05 and the maximum critical temperature Tc~32 K has been obtained in samples…
Enhancing the critical temperature (TC) is important not only to the practical applications but also to the theories of superconductivity. MgB2 is a type II superconductor with a TC of 39 K, which is very close to the McMillan limit.…
The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with…
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a…
Polycrystalline samples of a new superconducting EuAsFeO0.85F0.15 compound with critical temperature Tc=11K were prepared by solid state synthesis. Its electric and magnetic properties have been investigated in magnetic fields from 0.1 to…
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have…
The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics…
The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training…
Recently, there have been many works on the deep learning of statistical ensembles to determine the critical temperature of a possible phase transition. We analyze the detailed structure of an optimized deep learning machine and prove the…
A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the…
An application of the general equation obtained in Part I to low critical temperature superconductors utilizing an "ad hoc" phononic theory is developed. Then, we arrive to a specific expression for the bounding energy as a function of…
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally-excited…
It is known that time-dependent perturbations can enhance superconductivity and increase the critical temperature. If this phenomenon happens to high-T_c superconductors, one could obtain room-temperature superconductors, but this is still…
Elemental materials provide clean and fundamental platforms for studying superconductivity. However, the highest superconducting critical temperature (Tc) yet observed in elements has not exceeded 30 K. Discovering elemental superconductors…
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high…
In this paper with study phase transitions of the $q$-state Potts model, through a number of unsupervised machine learning techniques, namely Principal Component Analysis (PCA), $k$-means clustering, Uniform Manifold Approximation and…
We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of…
Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and…